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This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. Find inside: regression models; balanced ANOVA with both fixed- and random-effects models; unbalanced data with both fixed- and random-effects models; covariance models; generalized linear models; multivariate models; and repeated measures. New in this edition: MIXED and GENMOD procedures, updated examples, new software-related features, and other new material. This book is part of the SAS Press program.

1. Acknowledgments
2. Chapter 1 Introduction
3. Chapter 2 Regression
1. 2.1 Introduction
2. 2.2 The REG Procedure
3. 2.3 The GLM Procedure
4. 2.4 Statistical Background
4. Chapter 3 Analysis of Variance for Balanced Data
1. 3.1 Introduction
2. 3.2 One- and Two-Sample Tests and Statistics
3. 3.3 The Comparison of Several Means: Analysis of Variance
1. 3.3.1 Terminology and Notation
2. 3.3.2 Using the ANOVA and GLM Procedures
3. 3.3.3 Multiple Comparisons and Preplanned Comparisons
4. 3.4 The Analysis of One-Way Classification of Data
5. 3.5 Randomized-Blocks Designs
6. 3.6 A Latin Square Design with Two Response Variables
7. 3.7 A Two-Way Factorial Experiment
5. Chapter 4 Analyzing Data with Random Effects
1. 4.1 Introduction
2. 4.2 Nested Classifications
3. 4.3 Blocked Designs with Random Blocks
1. 4.3.1 Random-Blocks Analysis Using PROC MIXED
2. 4.3.2 Differences between GLM and MIXED Randomized-Complete-Blocks Analysis: Fixed versus Random Blocks
4. 4.4 The Two-Way Mixed Model
5. 4.5 A Classification with Both Crossed and Nested Effects
6. 4.6 Split-Plot Experiments
1. 4.6.1 A Standard Split-Plot Experiment
6. Chapter 5 Unbalanced Data Analysis: Basic Methods
1. 5.1 Introduction
2. 5.2 Applied Concepts of Analyzing Unbalanced Data
3. 5.3 Issues Associated with Empty Cells
4. 5.4 Some Problems with Unbalanced Mixed-Model Data
5. 5.5 Using the GLM Procedure to Analyze Unbalanced Mixed-Model Data
6. 5.6 Using the MIXED Procedure to Analyze Unbalanced Mixed-Model Data
7. 5.7 Using the GLM and MIXED Procedures to Analyze Mixed-Model Data with Empty Cells
8. 5.8 Summary and Conclusions about Using the GLM and MIXED Procedures to Analyze Unbalanced Mixed-Model Data
7. Chapter 6 Understanding Linear Models Concepts
1. 6.1 Introduction
2. 6.2 The Dummy-Variable Model
3. 6.3 Two-Way Classification: Unbalanced Data
1. 6.3.1 General Considerations
2. 6.3.2 Sums of Squares Computed by PROC GLM
3. 6.3.3 Interpreting Sums of Squares in Reduction Notation
4. 6.3.4 Interpreting Sums of Squares in -Model Notation
5. 6.3.5 An Example of Unbalanced Two-Way Classification
6. 6.3.6 The MEANS, LSMEANS, CONTRAST, and ESTIMATE Statements in a Two-Way Layout
7. 6.3.7 Estimable Functions for a Two-Way Classification
8. 6.3.8 Empty Cells
4. 6.4 Mixed-Model Issues
5. 6.5 ANOVA Issues for Unbalanced Mixed Models
6. 6.6 GLS and Likelihood Methodology Mixed Model
8. Chapter 7 Analysis of Covariance
1. 7.1 Introduction
2. 7.2 A One-Way Structure
3. 7.3 Unequal Slopes
4. 7.4 A Two-Way Structure without Interaction
5. 7.5 A Two-Way Structure with Interaction
6. 7.6 Orthogonal Polynomials and Covariance Methods
9. Chapter 8 Repeated-Measures Analysis
1. 8.1 Introduction
2. 8.2 The Univariate ANOVA Method for Analyzing Repeated Measures
3. 8.3 Multivariate and Univariate Methods Based on Contrasts of the Repeated Measures
4. 8.4 Mixed-Model Analysis of Repeated Measures
10. Chapter 9 Multivariate Linear Models
11. Chapter 10 Generalized Linear Models
1. 10.1 Introduction
2. 10.2 The Logistic and Probit Regression Models
3. 10.3 Binomial Models for Analysis of Variance and Analysis of Covariance
4. 10.4 Count Data and Overdispersion
5. 10.5 Generalized Linear Models with Repeated Measures&#8212;Generalized Estimating Equations
6. 10.6 Background Theory
12. Chapter 11 Examples of Special Applications
1. 11.1 Introduction
2. 11.2 Confounding in a Factorial Experiment
3. 11.3 A Balanced Incomplete-Blocks Design
4. 11.4 A Crossover Design with Residual Effects
5. 11.5 Models for Experiments with Qualitative and Quantitative Variables
6. 11.6 A Lack-of-Fit Analysis
7. 11.7 An Unbalanced Nested Structure
8. 11.8 An Analysis of Multi-Location Data
9. 11.9 Absorbing Nesting Effects
13. References
14. Index